Vehicle Feature Map Aggregation With Pose Error Correction
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately navigating and communicating due to pose errors in intermediate environmental representations shared among vehicles, leading to inaccuracies in object detection and prediction.
Innovation Solution
A method involving machine-learned models that aggregate sensor data from multiple autonomous vehicles to correct pose errors by determining relative and absolute poses, generating updated intermediate environmental representations, and producing autonomy outputs for improved navigation and prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If autonomous vehicles share intermediate environmental representations with other vehicles, then the coverage and information availability improve, but pose errors accumulate and propagate across the aggregated feature map
Solution Approach 1:
The system implements feedback by detecting pose errors in received intermediate environmental representations and using these detections to correct the errors before integrating the data into the local aggregated feature map. This closed-loop approach ensures that information from multiple vehicles is incorporated while maintaining pose accuracy.
Solution Approach 2:
The system introduces an intermediary correction mechanism that processes intermediate environmental representations from other vehicles. This intermediary layer detects pose errors and applies corrections, acting as a buffer between raw incoming data and the final aggregated feature map, thereby preventing error propagation.
2Measurement precision
If pose errors are corrected using detected features and messages from other vehicles, then the accuracy of the aggregated feature map improves, but the computational complexity and processing time increase
Solution Approach 1:
The system performs preliminary actions by detecting pose errors and determining corrections before fully integrating intermediate environmental representations into the aggregated feature map. This pre-processing approach ensures accuracy while managing computational load by preparing corrections in advance rather than during real-time integration.
Solution Approach 2:
The system applies partial correction by focusing computational resources on correcting only the pose errors in received messages from other vehicles, rather than reprocessing all environmental data. This selective correction approach improves pose precision without requiring excessive computational complexity.
Data Source
AI summary
Systems and methods for improved vehicle-to-vehicle communications are provided. A system can obtain sensor data depicting its surrounding environment and input the sensor data (or processed sensor data) to a machine-learned model to perceive its surrounding environment based on its location within the environment. The machine-learned model can generate an intermediate environmental representation that encodes features within the surrounding environment. The system can receive a number of different intermediate environmental representations and corresponding locations from various other systems, aggregate the representations based on the corresponding locations, and perceive its surrounding environment based on the aggregated representations. The system can determine relative poses between the each of the systems and an absolute pose for each system based on the representations. Each representation can be aggregated based on the relative or absolute poses of each system and weighted according to an estimated accuracy of the location corresponding to the representation.